Intrusion Detection In Mobile Ad Hoc Networks Research Articles

Multi head self-attention gated graph convolutional network based multi‑attack intrusion detection in MANET

Designing of intrusion detection system (IDS), and mobile ad hoc networks (MANET) prevention technique with examined detection rate, memory consumption with minimum overhead are vital concerns. Node mobility and node energy are the two optimization problems in MANETs wherein nodes travel uncertainly in any direction, evolving in a continuous change of topology. With the proposed approach, a Centrality Coati Optimization Algorithm based Cluster Gradient for multi attack intrusion identification is devised. This study focuses on the problems of node mobility and energy to develop a clustering algorithm for cluster head selection in MANET that is incited by Dual Network Centrality. Compact cluster formation is carried out by Coati Optimization Algorithm (COA). The Multi-head Self-Attention based Gated Graph Convolutional Network (MSA-GCNN) with a hybrid type of IDS recognizes several attacks, including DoS and Zero-Day attacks. The proposed technique is implemented in NS-2 network simulator. The performance of proposed approach is examined under some parameters, like attack detection rate, memory consumption, computational time for detecting the intruder. The outcomes display that the proposed technique decreases the IDS traffic and entire consumption of memory and sustains a higher attack identification rate with less computational time. The proposed technique attains 4.299 %, 10.375 % and 6.935 % Accuracy, 5.262 %, 8.375 % and 7.945 % Precision, 7.282 %, 10.365 % and 5.935 % Recall, 9.272 %, 5.355 % and 8.965 % ROC is higher compared with the existing methods such as, Epsilon Swarm Optimized Cluster Gradient along deep belief classifier for multiple attack intrusion detection (ESOC-MA-ID-MANET), Intrusion Detection secure solution for intrusion detection in cloud computing utilizing hybrid deep learning approach called EOS-IDS and improved heap optimization (IHO-MA-ID-MANET) for induction detection technique respectively.

Epilson Swarm Optimized Cluster Gradient and deep belief classifier for multi-attack intrusion detection in MANET

Design of intrusion detection, and MANET prevention mechanism, with scrutinized detection rate, memory consumption with minimal overhead are crucial research concerns. Node mobility and energy of the node are dual essential optimization issues in mobile ad hoc networks (MANETs) where nodes traverse uncertainly in any direction, evolving in topology's continuing modification. A Centrality Epilson Greedy Swarm and Gradient Deep Belief Classifier (CEGS-GDBC) for multi-attack intrusion detection are designed with the proposed method. The paper concentrates on the issues of node mobility and energy to emerge a clustering algorithm inspired by Dual Network Centrality for cluster head election in MANET. Compact cluster formation is done with the help of Epilson Greedy Swarm Optimization. Finally, with a hybrid type of IDS, Gradient using the Deep Belief Network Classifier identifies multi-attack, i.e., DoS and Zero-Day attack. The proposed work is experimented extensively in the NS-2 network simulator and compared with the other existing algorithms. The proposed method's performance is studied in terms of different parameters such as attack detection rate, memory consumption, and computational time for identifying and isolating the intruder. Simulation results show that the proposed method extensively minimizes the IDS traffic and overall memory consumption and maintains a high attack detection rate with minimal computational time. From the results, CEGS-GDBC method increases the attack detection rate by 31% and reduces the memory consumption and computational time by 39% and 41% as compared to Fuzzy elephant—Herd optimization and Cross centric intrusion detection system.

An approach for optimal-secure multi-path routing and intrusion detection in MANET

Mobile ad-hoc network (MANET) is dynamic in nature that is susceptible to energy and security constraints. Among most of the existing techniques, energy optimization was a hectic challenge, which is addressed effectively using the routing protocols. Accordingly, this paper proposes an effective multipath routing protocol in MANET based on an optimization algorithm. The energy and the security crisis in the MANET are addressed effectively using the cluster head (CH) selection and intrusion detection strategies namely, fuzzy clustering and fuzzy Naive Bayes (fuzzy NB). Then, the multipath routing progresses using the secure nodes based on the routing protocol, Bird swarm-whale optimization algorithm (BSWOA), which is the integration of bird swarm optimization (BSA) in whale optimization algorithm (WOA). The selection of the optimal routes is based on fitness factors, such as connectivity, energy, trust, and throughput. The analysis of the methods is done using the attacks, such as flooding, blackhole, and selective packet drop based on the performance metrics. The proposed BSWOA acquired the maximal energy, throughput, detection rate, and a minimal delay of 9.48 Joule, 0.676 bps, 69.9%, and 0.00372 ms in the presence of the attack.

A Review on Cooperative Bait Based Intrusion Detection in MANET

Mobile Ad Hoc Network (MANET) is a kind of wireless network where a number of mobile nodes are wirelessly connected without any fixed infrastructure. Several attacks or intrusions may occur because of the presence of selfish or malicious nodes in MANETs while performing data transmission from one mobile node to another. Vulnerability occurs in the mobile nodes of the MANETs, when an intruder node easily captures the traffic pattern of the network. A Cooperative Bait Detection Scheme is introduced for detecting the selfish node and malicious node in MANET. CBDS contains the more benefit for proactive and reactive protection constructions using routing scheme to identify the black hole attacks in MANETs. With the design of CBDS, the source node selects the adjacent node address which is taken as bait destination address for transmitting bait malicious node to reply message. Then the malicious nodes are detected from participating the routing operation based on reverse tracing technique for providing better security.

Enhanced replica detection scheme for efficient analysis of intrusion detection in MANET

Nowadays, detection scheme of intrusion is placing a major role for efficient access and analysis in Mobile Ad-hoc network (MANET). In the past, the detection scheme of Intrusion was used to identify the efficiency of the network and in maximum systems it performs with huge rate of false alarm. In this paper, an Effective approach of the Enhanced Replica Detection scheme (ERDS) based on Sequential Probability Ratio Test (SPRT) is proposed to detect the malicious actions and to have a secure path without claim in an efficient manner. Also, provides strategies to avoid attacker and to provide secure communication. In order to have an efficient analysis of intrusion detection the proposed approach is implemented based on the anomaly. To achieve this, the detection scheme is established based on SPRT and demonstrated the performances of detection with less claim. The simulation results of control overhead, packet delivery ratio, efficient detection, energy consumption and average claims are carried out for the analysis of performance to show the improvement than the existing by using the network simulator tool. Also, the performance of the proposed system illustrated the detection of intrusion in the normal and attacker states of the network.

FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET.

Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs) significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS) and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM) for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV) protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-the-art techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks.

RWD based IDS for MANET

Mobile Ad-Hoc network (MANET) is a temporary infrastructure less network. This network is formed by combining some set of wireless mobile hosts [1] [5]. The host is called as a node which dynamically establishes their own network. Intrusion detection in MANETs, however, is challenging for a number of reasons. These networks change their topologies dynamically due to node mobility; lack concentration points where traffic can be analyzed for intrusions; utilize self-configuring multi-party infrastructure protocols that are susceptible to malicious manipulation; and rely on wireless communications channels that provide limited bandwidth .To overcome these constraints, researchers have proposed a number of intrusion detection approaches specifically for MANETs. Intrusion detection is a process of identifying and responding to malicious activity targeted at computing and networking resources. In this IDS architecture multilayer specification based detection engine is used along with Random Walker Detector (RWD) [1] [16]. It randomly traverses a network and find outs that on which node which attack is occurred. If there is attacks it performs re-routing.

Enhancing Efficiency of Intrusion Detection Based on Intelligent Immune Method in MANET

In order to enhance efficiency of intrusion detection in the Mobile Ad hoc NETwork (MANET), an intrusion prediction method based on intelligent immune threshold matching algorithm was presented. Using a dynamic load-balancing algorithm, wireless network data packet was distributed to a set of analysis subsystem by the balance subsystem; it could avoid packet loss and false negatives in high-performance wireless network with handling heavy traffic loads in real-time. In addition, adopting the dynamic threshold value, which was generated from variable network speed, the mature antibody could better match the antigen of the database subsystem, and consequently the accuracy of detection was increased. Experiment shows this intrusion detection method has relatively low false positive rate and false negative rate, so it effectively resolves the shortage of intrusion detection in MANET.

On effective data aggregation techniques in host-based intrusion detection in MANET

A Mobile Ad Hoc Network (MANET) has been used in both commercial and tactical applications and faces security risks. Conducting cyber–attack monitoring and detection in MANET is challenging owning to limited resources. To deal with this issue, in this paper we develop lossless and lossy aggregation techniques to reduce the resources (e.g., energy consumption and bandwidth use) for transmitting security information through MANET while preserving the desired detection accuracy for cyber security operation. Particularly, our novel compression–based data aggregation technique effectively removes the duplicated information and compresses the pivotal data. The event–based data aggregation consolidates the data with the same type of predefined events. We also develop lossy data aggregation technique, namely feature–based data aggregation, which defines a series of basic features. We conduct real–world experiments and simulations to evaluate the effectiveness of our proposed data aggregation techniques in terms of energy consumption and detection accuracy.

Evaluation of classification algorithms for intrusion detection in MANETs

Mobile Ad hoc Networks (MANETs) are wireless networks without fixed infrastructure based on the cooperation of independent mobile nodes. The proliferation of these networks and their use in critical scenarios (like battlefield communications or vehicular networks) require new security mechanisms and policies to guarantee the integrity, confidentiality and availability of the data transmitted. Intrusion Detection Systems used in wired networks are inappropriate in this kind of networks since different vulnerabilities may appear due to resource constraints of the participating nodes and the nature of the communication. This article presents a comparison of the effectiveness of six different classifiers to detect malicious activities in MANETs. Results show that Genetic Programming and Support Vector Machines may help considerably in detecting malicious activities in MANETs.

Intrusion detection in MANET using classification algorithms: The effects of cost and model selection

Intrusion detection is frequently used as a second line of defense in Mobile Ad-hoc Networks (MANETs). In this paper we examine how to properly use classification methods in intrusion detection for MANETs. In order to do so we evaluate five supervised classification algorithms for intrusion detection on a number of metrics. We measure their performance on a dataset, described in this paper, which includes varied traffic conditions and mobility patterns for multiple attacks. One of our goals is to investigate how classification performance depends on the problem cost matrix. Consequently, we examine how the use of uniform versusweighted cost matrices affects classifier performance. A second goal is to examine techniques for tuning classifiers when unknown attack subtypes are expected during testing. Frequently, when classifiers are tuned using cross-validation, data from the same types of attacks are available in all folds. This differs from real-world employment where unknown types of attacks may be present. Consequently, we develop a sequential cross-validation procedure so that not all types of attacks will necessarily be present across all folds, in the hope that this would make the tuning of classifiers more robust. Our results indicate that weighted cost matrices can be used effectively with most statistical classifiers and that sequential cross-validation can have a small, but significant effect for certain types of classifiers.

Game theory approaches for improving intrusion detection in MANETs

Mobile Ad Hoc Networks (MANETs) are wireless networks in which the mobile nodes exchange information without the help of any predefined infrastructure. In such networks also called spontaneous networks, the nodes collaborate to provide the basic network services. Due to their communication type and resources constraint, MANETs are vulnerable to diverse types of attacks and intrusions. One of the security mechanisms for MANETs is intrusion detection that consists in taking action on demand to mitigate intrusions. In this paper, we investigate intrusion detection in MANETs and some different solutions of this problem based on the approach of game theory. Game theory is a theory of decision making under conditions of uncertainty and interdependence. Game theory is a powerful mathematical tool that analyzes the strategic interactions among multiple decision makers and the results of researches show that this subject improves the network performance. The aim of this paper is comparing some of existing methods that can be used to gain optimal intrusion detection for MANETs. Key words: Mobile ad hoc network, intrusion detection system, game theory, life time.

Mechanism Design-Based Secure Leader Election Model for Intrusion Detection in MANET

In this paper, we study leader election in the presence of selfish nodes for intrusion detection in mobile ad hoc networks (MANETs). To balance the resource consumption among all nodes and prolong the lifetime of an MANET, nodes with the most remaining resources should be elected as the leaders. However, there are two main obstacles in achieving this goal. First, without incentives for serving others, a node might behave selfishly by lying about its remaining resources and avoiding being elected. Second, electing an optimal collection of leaders to minimize the overall resource consumption may incur a prohibitive performance overhead, if such an election requires flooding the network. To address the issue of selfish nodes, we present a solution based on mechanism design theory. More specifically, the solution provides nodes with incentives in the form of reputations to encourage nodes in honestly participating in the election process. The amount of incentives is based on the Vickrey, Clarke, and Groves (VCG) model to ensure truth-telling to be the dominant strategy for any node. To address the optimal election issue, we propose a series of local election algorithms that can lead to globally optimal election results with a low cost. We address these issues in two possible application settings, namely, Cluster-Dependent Leader Election (CDLE) and Cluster-Independent Leader Election (CILE). The former assumes given clusters of nodes, whereas the latter does not require any preclustering. Finally, we justify the effectiveness of the proposed schemes through extensive experiments.

A comparative evaluation of intrusion detection architectures for mobile ad hoc networks

Mobile Ad Hoc Networks (MANETs) are susceptible to a variety of attacks that threaten their operation and the provided services. Intrusion Detection Systems (IDSs) may act as defensive mechanisms, since they monitor network activities in order to detect malicious actions performed by intruders, and then initiate the appropriate countermeasures. IDS for MANETs have attracted much attention recently and thus, there are many publications that propose new IDS solutions or improvements to the existing. This paper evaluates and compares the most prominent IDS architectures for MANETs. IDS architectures are defined as the operational structures of IDSs. For each IDS, the architecture and the related functionality are briefly presented and analyzed focusing on both the operational strengths and weaknesses. Moreover, methods/techniques that have been proposed to improve the performance and the provided security services of those are evaluated and their shortcomings or weaknesses are presented. A comparison of the studied IDS architectures is carried out using a set of critical evaluation metrics, which derive from: (i) the deployment, architectural, and operational characteristics of MANETs; (ii) the special requirements of intrusion detection in MANETs; and (iii) the carried analysis that reveals the most important strengths and weaknesses of the existing IDS architectures. The evaluation metrics of IDSs are divided into two groups: the first one is related to performance and the second to security. Finally, based on the carried evaluation and comparison a set of design features and principles are presented, which have to be addressed and satisfied in future research of designing and implementing IDSs for MANETs.